Redis Data Integration’s (RDI) data transformation capabilities allow users to transform their data beyond the default translation of source types to Redis types. The transformation involves no coding. Instead, it is described in a set of human readable YAML files, one per source table.

The ingested format and types are different from one source to another. Currently, the only supported source is Debezium. The first transformation from Debezium types to native JSON with Redis types is done automatically without any need for user instructions. Then, this JSON is passed on to the user defined transformation pipeline.

Each job describes the transformation logic to perform on data from a single source. The source is typically a database table or collection and is specified as the full name of this table/collection. The job may include filtering logic to skip data that matches a condition. Other logical steps in the job will transform the data into the desired output that will be stored in Redis as hashes or JSON.

Data transformation pipeline, high level

Default job

In situations where there is a need to perform a transformation on all ingested records without creating a specific job for specific tables, the default job is used. The transformation associated with this job will be applied to all tables that lack their own explicitly defined jobs. The default job must have a table name of “*”, and only one instance of this type of job is permitted.

For example, the default job can streamline tasks such as adding a prefix or postfix to Redis keys, or adding fields to new hashes and JSONs without customizing each source table.

Currently, the default job is supported for ingest pipelines only.


This example demonstrates the process of adding an app_code field with a value of foo using the add_field block to all tables that lack explicitly defined jobs. Additionally, it appends an aws prefix and a gcp postfix to every generated hash key.


  table: "*"
  row_format: full
  - uses: add_field
        - field: after.app_code
          expression: "`foo`"
          language: jmespath
  - uses: redis.write
      data_type: hash
        expression: concat(['aws', '#', table, '#', keys(key)[0], '#', values(key)[0], '#gcp'])
        language: jmespath


Each job is defined in a separate YAML file. All of these files will be uploaded to RDI using the deploy command. For more information, see deploy configuration). If you are using the scaffold command, place the job files in the jobs folder.

Job YAML structure


  • source:

    This section describes the table that the job operates on:

    • server_name: logical server name (optional). Corresponds to the debezium.source.topic.prefix property specified in Debezium Server’s config file
    • db: database name (optional)
    • schema: database schema (optional)
    • table: database table name
    • row_format: format of the data to be transformed: data_only (default) - only payload, full - complete change record

Note: Any reference to the properties server_name, db, schema, and table will be treated by default as case insensitive. This can be changed by setting case_insensitive to false.

Cassandra only: In Cassandra, a keyspace is roughly the equivalent to a schema in other databases. RDI uses the schema property declared in a job file to match the keyspace attribute of the incoming change record.

MongoDB only: In MongoDB, a replica set is a cluster of shards with data and can be regarded as roughly equivalent to a schema in a relational database. A MongoDB collection is similar to a table in other databases. RDI uses the schema and table properties declared in a job file to match the replica set and collection attributes of the incoming change record, respectively.

  • transform:

    This section includes a series of blocks that define how the data will be transformed. For more information, see supported blocks and JMESPath custom functions.

  • output:

    This section defines the output targets for processed data:

    • Cassandra:
      • uses: cassandra.write: write into a Cassandra data store
      • with:
        • connection: connection name
        • keyspace: keyspace
        • table: target table
        • keys: array of key columns
        • mapping: array of mapping columns
        • opcode_field: the name of the field in the payload that holds the operation (c - create, d - delete, u - update) for this record in the database
    • Redis:
      • uses: redis.write: write to a Redis data structure. Multiple blocks of this type are allowed in the same job
      • with:
        • connection: connection name as defined in config.yaml (by default, the connection named ‘target’ is used)
        • data_type: target data structure when writing data to Redis (hash, json, set and stream are supported values)
        • key: this allows you to override the key of the record by applying custom logic:
          • expression: expression to execute
          • language: expression language, JMESPath or SQL
        • expire: positive integer value indicating a number of seconds for the key to expire. If not set, the key will never expire
    • SQL:
      • uses: relational.write: write into a SQL-compatible data store
      • with:
        • connection: connection name
        • schema: schema
        • table: target table name
        • keys: array of key columns
        • mapping: array of mapping columns
        • opcode_field: the name of the field in the payload that holds the operation (c - create, d - delete, u - update) for this record in the database


  • source is required.
  • Either transform, key, or both should be specified.

Using key in transformations

To access the Redis key (for example in a write-behind job) you will need to take the following steps:

  • Set row_format: full to allow access to the key that is part of the full data entry.
  • Use the expression key.key to get the Redis key as a string.

Before and after values

Update events typically report before and after sections, providing access to the data state before and after the update. To access the “before” values explicitly, you will need to:

  • Set row_format: full to allow access to the key that is part of the full data entry.
  • Use the before.<FIELD_NAME> pattern.


This example shows how to rename the fname field to first_name in the table emp using the rename_field block. It also demonstrates how to set the key of this record instead of relying on the default logic.


  server_name: redislabs
  schema: dbo
  table: emp
  - uses: rename_field
      from_field: fname
      to_field: first_name
  - uses: redis.write
      connection: target
        expression: concat(['emp:fname:',fname,':lname:',lname])
        language: jmespath

Deploy configuration

In order to deploy your jobs to the remote RDI database, run:

redis-di deploy

Deploy configuration on Kubernetes

If the RDI CLI is deployed as a pod in a Kubernetes cluster, perform these steps to deploy your jobs:

  • Create a ConfigMap from the YAML files in your jobs folder:

    kubectl create configmap redis-di-jobs --from-file=jobs/
  • Deploy your jobs:

    kubectl exec -it pod/redis-di-cli -- redis-di deploy

Note: A delay occurs between creating/modifying the ConfigMap and its availability in the redis-di-cli pod. Wait around 30 seconds before running the redis-di deploy command.

You have two options to update the ConfigMap:

  • For smaller changes, you can edit the ConfigMap directly with this command:

    kubectl edit configmap redis-di-jobs
  • For bigger changes such as adding another job file, edit the files in your local jobs folder and then run this command:

    kubectl create configmap redis-di-jobs --from-file=jobs/ --dry-run=client -o yaml | kubectl apply -f -

Note: You need to run kubectl exec -it pod/redis-di-cli -- redis-di deploy after updating the ConfigMap with either option.